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In order to avoid conventional controlling methods which created obstaclesdue to the complexity of systems and intense demand on data density, developingmodern and more efficient control methods are required. In this way,reinforcement learning off-policy and model-free algorithms help to avoidworking with complex models. In terms of speed and accuracy, they becomeprominent methods because the algorithms use their past experience to learn theoptimal policies. In this study, three reinforcement learning algorithms; DDPG,TD3 and SAC have been used to train Fetch robotic manipulator for fourdifferent tasks in MuJoCo simulation environment. All of these algorithms areoff-policy and able to achieve their desired target by optimizing both policyand value functions. In the current study, the efficiency and the speed ofthese three algorithms are analyzed in a controlled environment.

Off-Policy Deep Reinforcement Learning Algorithms for Handling Various Robotic Manipulator Tasks